A genetic algorithm (GA) is hybridized with an artificial immune system (AIS) as an alternative to tackle constrained optimization problems in engineering. The AIS is inspired in the clonal selection principle and is embedded into a standard GA search engine in order to help move the population into the feasible region. The procedure is applied to mechanical engineering problems available in the literature and compared to other alternative techniques.

Performance profiles are an analytical tool for the visualization and interpretation of the results of benchmark experiments. In this paper we discuss their explanatory power, and argue that they should be more widely used by the evolutionary computation community. We also introduce some novel performance measures which can be extracted from the performance profiles. In order to illustrate their potential, we apply the referred profiles to the analysis of the results of the CEC 2006 constrained optimization competition. While some of the results are corroborated, some new facts are pointed out and additional conclusions are drawn.

A genetic algorithm (GA) is hybridized with an artificial immune system (AIS) as an alternative to tackle constrained optimization problems in engineering. The AIS is inspired in the clonal selection principle and is embedded into a standard GA search engine in order to help move the population into the feasible region. The procedure is applied to mechanical engineering problems available in the literature and compared to other alternative techniques.